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Feature maps of all convolutional 2D layers; (a) Conv2d layer feature map (b) Conv2d_1 layer festure map (c) Conv2d_2 feature map (c) Conv2d_3 layer feature map (d) Conv2d_4 layer feature I Conv2d_5 layer feature map.
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It is estimated that about 5–7 million children die from pneumonia every year. Pneumonia affects a large proportion of the world's population, approximately 7%, and is one of the most common respiratory illnesses in humans. Chest X-Ray is the most frequent way of diagnosing pneumonia. However, in certain countries, there is a scarcity of qualified...
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Citations
... CNN architectures are built using a variety of building blocks, such as Fully-Connected (FC) layers, Pooling layers, and Convolution layers. Convolution layers, which combine linear and nonlinear operations-that is, activation functions and convolution operations-are used in feature extraction [24,25]. Kernels and their hyperparameters, such as the size, quantity, stride, padding, and activation function of each kernel, are the parameters of convolution layers [26]. ...
... The final FC layer's activation function is usually SoftMax for the categorization of multiple classes and Sigmoid for binary classification. The node values in the final FC layer of the proposed model has computed using (4), and the sigmoid activation function for a binary categorization dataset-Ⅰ is calculated using (5) [24]. ...
... Additionally, the component of the class rating vector in the final FC layer is displayed by . The category with the highest coefficient is chosen as the output class in the SoftMax activation function [24]. A backpropagation algorithm has used during CNN training to adjust the weights of the FC and convolution layers. ...
Brain tumors are frequently classified with high accuracy using convolutional neural networks (CNNs) and better comprehend the spatial connections among pixels in complex pictures. Due to their tiny receptive fields, the majority of deep convolutional neural network (DCNN)-based techniques overfit and are unable to extract global context information from more significant regions. While dilated convolution retains data resolution at the output layer and increases the receptive field without adding computation, stacking several dilated convolutions has the drawback of producing a grid effect. To handle gridding artifacts and extract both coarse and fine features from the images, this research suggests using a dilated parallel deep convolutional neural network (PDCNN) architecture that preserves a wide receptive field. To reduce complexity, initially, input images are resized and then grayscale transformed. Data augmentation has since been used to expand the number of datasets. Dilated PDCNN makes use of the lower computational overhead and contributes to the reduction of gridding artifacts. By contrasting various dilation rates, the global path uses a low dilation rate (2,1,1), while the local path uses a high dilation rate (4,2,1) for decremental even numbers to tackle gridding artifacts and extract both coarse and fine features from the two parallel paths. Using three different types of MRI datasets, the suggested dilated PDCNN with the average ensemble method performs better. The accuracy provided by the Multiclass Kaggle dataset-III, Figshare dataset-II, and Binary tumor identification dataset-I is 98.35%, 98.13%, and 98.67%, respectively. In comparison to state-of-the-art techniques, the suggested structure improves results by extracting both fine and coarse features, making it efficient.
... The accuracy achieved by the model in the second diagnostic was 94.2%, and in the third diagnostic, it was 91.4%, outperforming the current state-of-the-art methods. Sourab et al. proposed a CNN architecture with 22 layers in their study [8], which utilized three different machine learning techniques (Support Vector Machine, Random Forest Classifier, and K-Nearest Neighbor) to extract and classify the CNN model's learned features. The dataset used in the study was Mendeley Data v2, consisting of 5856 chest x-ray images in both Pneumonia and Normal classes. ...
Diagnosing lung inflammation, particularly pneumonia, is of paramount importance for effectively treating and managing the disease. Pneumonia is a common respiratory infection caused by bacteria, viruses, or fungi and can indiscriminately affect people of all ages. As highlighted by the World Health Organization (WHO), this prevalent disease tragically accounts for a substantial 15% of global mortality in children under five years of age. This article presents a comparative study of the Inception-ResNet deep learning model's performance in diagnosing pneumonia from chest radiographs. The study leverages Mendeley's chest X-ray images dataset, which contains 5856 2D images, including both Viral and Bacterial Pneumonia X-ray images. The Inception-ResNet model is compared with seven other state-of-the-art convolutional neural networks (CNNs), and the experimental results demonstrate the Inception-ResNet model's superiority in extracting essential features and saving computation runtime. Furthermore, we examine the impact of transfer learning with fine-tuning in improving the performance of deep convolutional models. This study provides valuable insights into using deep learning models for pneumonia diagnosis and highlights the potential of the Inception-ResNet model in this field. In classification accuracy, Inception-ResNet-V2 showed superior performance compared to other models, including ResNet152V2, MobileNet-V3 (Large and Small), EfficientNetV2 (Large and Small), InceptionV3, and NASNet-Mobile, with substantial margins. It outperformed them by 2.6%, 6.5%, 7.1%, 13%, 16.1%, 3.9%, and 1.6%, respectively, demonstrating its significant advantage in accurate classification. Abstract Diagnosing lung inflammation, particularly pneumonia, is of paramount importance for effectively treating and managing the disease. Pneumonia is a common respiratory infection caused by bacteria, viruses, or fungi and can indiscriminately affect people of all ages. As highlighted by the World Health Organization (WHO), this prevalent disease tragically accounts for a substantial 15% of global mortality in children under five years of age. This article presents a comparative study of the Inception-ResNet deep learning model's performance in diagnosing pneumonia from chest radiographs. The study leverages Mendeley's chest X-ray images dataset, which contains 5856 2D images, including both Viral and Bacterial Pneumonia X-ray images. The Inception-ResNet model is compared with seven other state-of-the-art convolutional neural networks (CNNs), and the experimental results demonstrate the Inception-ResNet model's superiority in extracting essential features and saving computation runtime. Furthermore, we examine the impact of transfer learning with fine-tuning in improving the performance of deep convolutional models. This study provides valuable insights into using deep learning models for pneumonia diagnosis and highlights the potential of the Inception-ResNet model in this field. In classification accuracy, Inception-ResNet-V2 showed superior performance compared to other models, including ResNet152V2, MobileNet-V3 (Large and Small), EfficientNetV2 (Large and Small), InceptionV3, and NASNet-Mobile, with substantial margins. It outperformed them by 2.6%, 6.5%, 7.1%, 13%, 16.1%, 3.9%, and 1.6%, respectively, demonstrating its significant advantage in accurate classification.
... QCNN [31] CNN-b 0 [32] ConvNets [33] CNN Dropout [34] CNN-b 1 [35] CNN-SVM [35] CNN-KNN [ ...
... QCNN [31] CNN-b 0 [32] ConvNets [33] CNN Dropout [34] CNN-b 1 [35] CNN-SVM [35] CNN-KNN [ ...
The Chest X-ray (CXR) is a commonly used diagnostic imaging test that requires significant expertise and careful observation due to the complex nature of the pathology and fine texture of lung lesions. Despite the long-term clinical training and professional guidance provided to radiologists, there is still the possibility of errors in diagnosis. Therefore, we have developed a novel approach using a convolutional neural network (CNN) model to detect the abnormalities of CXR images. The model was optimized using algorithms such as Adam and RMSprop. Also, several hyperparameters were optimized, including the pooling layer, convolutional layer, dropout layer, target size, and epochs. Hyperparameter optimization aims to improve the model's accuracy by testing various combinations of hyperparameter values and optimization algorithms. To evaluate the model's performance, we used scenario modeling to create 32 models and tested them using a confusion matrix. The results indicated that the best accuracy achieved by the model was 97.94%. This accuracy was based on training and test data using 4538 CXR images. The findings suggest that hyperparameter optimization can improve the CNN model's accuracy in accurately identifying CXR abnormalities. Therefore, this study has important implications for improving the accuracy and reliability of CXR image interpretation, which could ultimately benefit patients by improving the detection and treatment of lung diseases. Acknowledging dataset constraints, we address future steps for model improvement.
... The author used chest X-ray images of children aged 1 to 5 to identify Pneumonia and also utilized optical coherence tomography to identify eye problems (OCT). Sourab et al. [15] suggested a novel hybrid approach. They proposed a CNN architecture with 22 layers to extract features from chest X-ray images for Pneumonia. ...
Medical imaging plays an important role in the medical sector in identifying diseases. X-ray, computed tomography (CT) scans, and magnetic resonance imaging (MRI) are a few examples of medical imaging. Most of the time, these imaging techniques are utilized to examine and diagnose diseases. Medical professionals identify the problem after analyzing the images. However, manual identification can be challenging because the human eye is not always able to recognize complex patterns in an image. Because of this, it is difficult for any professional to recognize a disease with rapidity and accuracy. In recent years, medical professionals have started adopting Computer-Aided Diagnosis (CAD) systems to evaluate medical images. This system can analyze the image and detect the disease very precisely and quickly. However, this system has certain drawbacks in that it needs to be processed before analysis. Medical research is already entered a new era of research which is called Artificial Intelligence (AI). AI can automatically find complex patterns from an image and identify diseases. Methods for medical imaging that uses AI techniques will be covered in this chapter.
... The accuracy achieved by the model in the second diagnostic was 94.2%, and in the third diagnostic, it was 91.4%, outperforming the current state-of-the-art methods. Sourab et al. proposed a CNN architecture with 22 layers in their study [8], which utilized three different machine learning techniques (Support Vector Machine, Random Forest Classifier, and K-Nearest Neighbor) to extract and classify the CNN model's learned features. The dataset used in the study was Mendeley Data v2, consisting of 5856 chest x-ray images in both Pneumonia and Normal classes. ...
Diagnosing lung inflammation, particularly pneumonia, is of paramount importance for effectively treating and managing the disease. Pneumonia is a common respiratory infection caused by bacteria, viruses, or fungi and can indiscriminately affect people of all ages. As highlighted by the World Health Organization (WHO), this prevalent disease tragically accounts for a substantial 15% of global mortality in children under five years of age. This article presents a comparative study of the Inception-ResNet deep learning model's performance in diagnosing pneumonia from chest radiographs. The study leverages Mendeley's chest X-ray images dataset, which contains 5856 2D images, including both Viral and Bacterial Pneumonia X-ray images. The Inception-ResNet model is compared with seven other state-of-the-art convolutional neural networks (CNNs), and the experimental results demonstrate the Inception-ResNet model's superiority in extracting essential features and saving computation runtime. Furthermore, we examine the impact of transfer learning with fine-tuning in improving the performance of deep convolutional models. This study provides valuable insights into using deep learning models for pneumonia diagnosis and highlights the potential of the Inception-ResNet model in this field. In classification accuracy, Inception-ResNet-V2 showed superior performance compared to other models, including ResNet152V2, MobileNet-V3 (Large and Small), EfficientNetV2 (Large and Small), InceptionV3, and NASNet-Mobile, with substantial margins. It outperformed them by 2.6%, 6.5%, 7.1%, 13%, 16.1%, 3.9%, and 1.6%, respectively, demonstrating its significant advantage in accurate classification.
... In [3], Covid19, Pneumonia, and Healthy conditions were classified with VGG19 architecture on MongoDB dataset and 97.11% accuracy was obtained. In [9], a 22layer convolutional neural network (CNN) as feature extractor and Support Vector Machine (SVM), Random Forest (RF) and KNearest Neighbor (KNN) as classifier were used on Mendeley Data v2 dataset. As a result of the study, an accuracy rate of 99.52% with CNN+RF, 96.55% with CNN+SVM and 97.32% with CNN+KNN was obtained. ...
Pneumonia is one of the leading diseases of child mortality in the world. The fastest imaging method for detecting pneumonia in chest X-rays. Examining X-ray images is carried out by expert radiologists. It is important to develop computer-aided diagnosis systems due to the difficulty of the images examined. In this study, DenseNet121, DenseNet169, ResNet50, ResNet101, MobileNetV2, VGG16, Xception and InceptionV3 deep learning models were used to classify chest X-ray images. Experiments were done on the chest X-ray dataset of 5856 labeled images with the proposed models, and the results were compared. Transfer learning models have generally achieved high success rates in the problem of detecting pneumonia from chest X-ray images. The Xception model performed best with 96.16% validation and 95.73% test accuracy. It has been seen that transfer learning models are successful in classification problems.
... The author used chest X-ray images of children aged 1 to 5 to identify Pneumonia and also utilized optical coherence tomography to identify eye problems (OCT). Sourab et al. [15] suggested a novel hybrid approach. They proposed a CNN architecture with 22 layers to extract features from chest X-ray images for Pneumonia. ...
Medical imaging plays an important role in the medical sector in identifying diseases. X-ray, computed tomography (CT) scans, and magnetic resonance imaging (MRI) are a few examples of medical imaging. Most of the time, these imaging techniques are utilized to examine and diagnose diseases. Medical professionals identify the problem after analyzing the images. However, manual identification can be challenging because the human eye is not always able to recognize complex patterns in an image. Because of this, it is difficult for any professional to recognize a disease with rapidity and accuracy. In recent years, medical professionals have started adopting Computer-Aided Diagnosis (CAD) systems to evaluate medical images. This system can analyze the image and detect the disease very precisely and quickly. However, this system has certain drawbacks in that it needs to be processed before analysis. Medical research is already entered a new era of research which is called Artificial Intelligence (AI). AI can automatically find complex patterns from an image and identify diseases. Methods for medical imaging that uses AI techniques will be covered in this chapter.
... Predictive information from IMI, shapely data from SHAP, and Grad-CAM maps are used to explore hidden features using the DL model, which achieved an accuracy of 94.31%. Ref. [33] proposed a CNN with 22-layer and three machine learning methods to analyze X-rays for detecting pneumonia. The overfitting problem was addressed with a dense layer regulator. ...
An infectious disease called tuberculosis (TB) exhibits pneumonia-like symptoms and traits. One of the most important methods for identifying and diagnosing pneumonia and tuberculosis is X-ray imaging. However, early discrimination is difficult for radiologists and doctors because of the similarities between pneumonia and tuberculosis. As a result, patients do not receive the proper care, which in turn does not prevent the disease from spreading. The goal of this study is to extract hybrid features using a variety of techniques in order to achieve promising results in differentiating between pneumonia and tuberculosis. In this study, several approaches for early identification and distinguishing tuberculosis from pneumonia were suggested. The first proposed system for differentiating between pneumonia and tuberculosis uses hybrid techniques, VGG16 + support vector machine (SVM) and ResNet18 + SVM. The second proposed system for distinguishing between pneumonia and tuberculosis uses an artificial neural network (ANN) based on integrating features of VGG16 and ResNet18, before and after reducing the high dimensions using the principal component analysis (PCA) method. The third proposed system for distinguishing between pneumonia and tuberculosis uses ANN based on integrating features of VGG16 and ResNet18 separately with handcrafted features extracted by local binary pattern (LBP), discrete wavelet transform (DWT) and gray level co-occurrence matrix (GLCM) algorithms. All the proposed systems have achieved superior results in the early differentiation between pneumonia and tuberculosis. An ANN based on the features of VGG16 with LBP, DWT and GLCM (LDG) reached an accuracy of 99.6%, sensitivity of 99.17%, specificity of 99.42%, precision of 99.63%, and an AUC of 99.58%.
... The CNN feature extraction uses 6 convolutional layers, then the results from the convolutional layers will be converted into a single vector in the flattened layer. The result of feature maps in the flattened layer will be processed by KNN classification [21]. The KNN algorithm is used to classify tuberculosis bacteria and non-tuberculosis bacteria. ...
Mycobacterium tuberculosis is a pathogenic bacterium that causes respiratory tract disease in the lungs, namely tuberculosis (TB). The problem is to find out the bacterial colonies when the observation is still done manually using a microscope with a magnification of 1000 times. It took a long time and was tiring for the observer's eye. Based on this background, an automatic detection system for Mycobacterium tuberculosis was designed. Mycobacterium tuberculosis image data were obtained from the Semarang City Health Center. The dataset used is 220 sputum images, which are divided into 180 training data and 40 testing data. The method used in this research is a combination of Convolutional Neural Network (CNN) and K-Nearest Neighbor (KNN). CNN is used for image feature extraction. Furthermore, the results of the CNN feature extraction are classified using the KNN. The results of the accuracy of the combination of CNN-KNN and CNN were also compared. The stages of the process are color transformation, feature extraction, and data training with CNN, then classification with KNN. The results of the classification test between CNN and the CNN-KNN combination show that the CNN-KNN combination is better. The result of CNN-KNN accuracy is 92.5%, while CNN's accuracy is 90%.
... In the first convolutional layer, 5*5 kernel and 16 channels are applied to input images, which produces feature maps. To locate the important features, the filters traverse throughout the entire image [33]. The formula for the output feature map produced from convolutional layer is ...
The novel coronavirus is the new member of the SARS family, which can cause mild to severe infection in the lungs and other vital organs like the heart, kidney and liver. For detecting COVID-19 from images, traditional ANN can be employed. This method begins by extracting the features and then feeding the features into a suitable classifier. The classification rate is not so high as feature extraction is dependent on the experimenters' expertise. To solve this drawback, a hybrid CNN-KNN-based model with 5-fold cross-validation is proposed to classify covid-19 or non-covid19 from CT scans of patients. At first, some pre-processing steps like contrast enhancement, median filtering, data augmentation, and image resizing are performed. Secondly, the entire dataset is divided into five equal sections or folds for training and testing. By doing 5-fold cross-validation, the generalization of the dataset is ensured and the overfitting of the network is prevented. The proposed CNN model consists of four convolutional layers, four max-pooling layers, and two fully connected layers combined with 23 layers. The CNN architecture is used as a feature extractor in this case. The features are taken from the CNN model's fourth convolutional layer and finally, the features are classified using K Nearest Neighbor rather than softmax for better accuracy. The proposed method is conducted over an augmented dataset of 4085 CT scan images. The average accuracy, precision, recall and F1 score of the proposed method after performing a 5-fold cross-validation is 98.26%, 99.42%,97.2% and 98.19%, respectively. The proposed method's accuracy is comparable with the existing works described further, where the state of the art and the custom CNN models were used. Hence, this proposed method can diagnose the COVID-19 patients with higher efficiency.
